You are creating a machine learning model. You have a dataset that contains null rows. You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null…

The correct answer is C. Remove entire row. When a dataset contains null rows - entire rows where all values are missing - the appropriate action in Azure Machine Learning Studio's Clean Missing Data module is 'Remove entire row.' These rows contribute no usable information to model training and should be dropped…

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Question

You are creating a machine learning model. You have a dataset that contains null rows. You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset. Which parameter should you use?

Options

AReplace with mean
BRemove entire column
CRemove entire row
DHot Deck

How the community answered

(23 responses)

A
4% (1)
B
9% (2)
C
87% (20)

Explanation

When a dataset contains null rows - entire rows where all values are missing - the appropriate action in Azure Machine Learning Studio's Clean Missing Data module is 'Remove entire row.' These rows contribute no usable information to model training and should be dropped entirely. 'Replace with mean' would impute values into an otherwise meaningless row. 'Remove entire column' would discard a whole feature even if only a few rows have missing values. 'Hot Deck' imputes values by borrowing from similar records, which is inappropriate when an entire row is null since there is no partial data to base similarity on.

Topics

#Data cleaning#Missing data#Azure Machine Learning Studio#Data pre-processing

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